EDBT 2026 Demo / reviewers in the wild / expert
Hyeungshik Jung
dblp:217/9426
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 50% Multimedia analysis and retrieval · 50% | |
| Human-computer interaction and pervasive computing
1 paper |
Personal fabrication and tangible interfaces · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
interactive data analysis |
0.3 | 1 | 2018 | RecipeScape: An Interactive Tool for Analyzing Cooking Instructions at Scale · CHI 2018 |
Multimedia analysis and retrieval › food computing
recipe analysis |
0.3 | 1 | 2018 | RecipeScape: An Interactive Tool for Analyzing Cooking Instructions at Scale · CHI 2018 |
Methods — techniques the papers use, named apart from their topics
text mining · 0.7procedural similarity · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | RecipeScape: An Interactive Tool for Analyzing Cooking Instructions at ScaleabstractFor cooking professionals and culinary students, understanding cooking instructions is an essential yet demanding task. Common tasks include categorizing different approaches to cooking a dish and identifying usage patterns of particular ingredients or cooking methods, all of which require extensive browsing and comparison of multiple recipes. However, no existing system provides support for such in-depth and at-scale analysis. We present RecipeScape, an interactive system for browsing and analyzing the hundreds of recipes of a single dish available online. We also introduce a computational pipeline that extracts cooking processes from recipe text and calculates a procedural similarity between them. To evaluate how RecipeScape supports culinary analysis at scale, we conducted a user study with cooking professionals and culinary students with 500 recipes for two different dishes. Results show that RecipeScape clusters recipes into distinct approaches, and captures notable usage patterns of ingredients and cooking actions. Minsuk Chang, Léonore V. Guillain, Hyeungshik Jung, Vivian M. Hare, Juho Kim 0001, Maneesh Agrawala |
CHI | 3 |